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RequestQueue queue = Volley.newRequestQueue(context);
//for POST requests, only the following line should be changed to 

StringRequest sr = new StringRequest(Request.Method.GET, "http://headers.jsontest.com/",
  new Response.Listener<String>() {
    @Override
    public void onResponse(String response) {
      Log.e("HttpClient", "success! response: " + response.toString());
    }
  },

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#If you want verbose Log from the volley library you have to use adb

adb -s DEVICE_ID shell setprop log.tag.Volley VERBOSE

#you can get your DEVICE_ID by
adb devices

#If you want to persist this setting use
adb -s DEVICE_ID shell setprop persist.log.tag.Volley VERBOSE

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/*
 * From fragment to activty:
 */
((YourActivityClassName)getActivity()).yourPublicMethod();

/* 
 * From activity to fragment:
 */
FragmentManager fm = getSupportFragmentManager();
//if you added fragment via layout xml

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package net.aldemir.myapp.camera;

import android.content.Context;
import android.content.res.Configuration;
import android.graphics.Point;
import android.hardware.Camera;
import android.os.Handler;
import android.util.AttributeSet;
import android.util.Log;
import android.view.Display;

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This sample demonstrates how to handle events when a window is focused or it loses focus.

Bu örnek bir bir pencereye odaklanıldığında veya başka pencere, uygulama, sekmeye odaklanıldığında oluşan olayların kullanımını gösterir.

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from captcha.image import ImageCaptcha
from scipy import misc
from matplotlib import pyplot as plt
import numpy as np

capgen = ImageCaptcha()

x = np.random.randint(0,99999)

im_bytes=capgen.generate(str(x))

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from matplotlib import pyplot as plt
import cv2

img = cv2.imread('/Users/mustafa/test.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

plt.imshow(gray)
plt.title('my picture')
plt.show()

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# MXNet uses channels_first data format while Tensorflow uses channels_last data format.

img.shape
# (3, 224, 224)

x = np.moveaxis(img, 0, 2)
x.shape
# (224, 224, 3)

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import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Input
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
import matplotlib.pyplot as plt
from keras import backend as K
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

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# edit jupyter_notebook_config.py 
# https://jupyter-notebook.readthedocs.io/en/latest/config.html

c.NotebookApp.allow_origin = '*' 
c.NotebookApp.ip = '0.0.0.0' #